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Article

An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer

by
Simona Violino
1,
Luciano Ortenzi
1,
Francesca Antonucci
1,
Federico Pallottino
1,*,
Cinzia Benincasa
2,
Simone Figorilli
1 and
Corrado Costa
1
1
Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia Agraria (CREA)—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, 00015 Monterotondo (Rome), Italy
2
Consiglio per la Ricerca in Agricoltura e l’analisi dell’economia Agraria (CREA)—Centro di Ricerca Olivicoltura, Frutticoltura e Agrumicoltura, Contrada Li Rocchi Vermicelli 83, 87036 Rende (CS), Italy
*
Author to whom correspondence should be addressed.
Foods 2020, 9(6), 834; https://doi.org/10.3390/foods9060834
Submission received: 8 June 2020 / Accepted: 17 June 2020 / Published: 25 June 2020
(This article belongs to the Special Issue Extra Virgin Olive Oil Quality, Safety, and Authenticity)
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Abstract

Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308–373 nm and 594–605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples’ origin and thus could help to preserve the “Made in Italy” from fraud and sophistication related to its commerce.
Keywords: VIS-NIR; ANN; made in Italy; minor components; pigments; antioxidants; non-destructive techniques; ready-to-use; spectral signature; artificial intelligence AI VIS-NIR; ANN; made in Italy; minor components; pigments; antioxidants; non-destructive techniques; ready-to-use; spectral signature; artificial intelligence AI
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MDPI and ACS Style

Violino, S.; Ortenzi, L.; Antonucci, F.; Pallottino, F.; Benincasa, C.; Figorilli, S.; Costa, C. An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer. Foods 2020, 9, 834. https://doi.org/10.3390/foods9060834

AMA Style

Violino S, Ortenzi L, Antonucci F, Pallottino F, Benincasa C, Figorilli S, Costa C. An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer. Foods. 2020; 9(6):834. https://doi.org/10.3390/foods9060834

Chicago/Turabian Style

Violino, Simona, Luciano Ortenzi, Francesca Antonucci, Federico Pallottino, Cinzia Benincasa, Simone Figorilli, and Corrado Costa. 2020. "An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer" Foods 9, no. 6: 834. https://doi.org/10.3390/foods9060834

APA Style

Violino, S., Ortenzi, L., Antonucci, F., Pallottino, F., Benincasa, C., Figorilli, S., & Costa, C. (2020). An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer. Foods, 9(6), 834. https://doi.org/10.3390/foods9060834

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